Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition
This addresses secure authentication for mobile users in heterogeneous conditions, but it is incremental as it builds on existing deep learning methods with a novel loss function.
The paper tackled the challenge of mobile periocular recognition in unconstrained environments with variations in sensors, illumination, and distance by proposing a heterogeneity aware loss function in a deep learning framework, achieving state-of-the-art results and improved cross-database generalizability.
Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric patterns in unconstrained environment including variations in mobile camera sensors, illumination conditions, and capture distance. To address the heterogeneous challenge, this research presents a novel heterogeneity aware loss function within a deep learning framework. The effectiveness of the proposed loss function is evaluated for periocular biometrics using the CSIP, IMP and VISOB mobile periocular databases. The results show that the proposed algorithm yields state-of-the-art results in a heterogeneous environment and improves generalizability for cross-database experiments.